1. Field of the Invention
The present invention relates to a control apparatus for an internal combustion gasoline engine with fuel injection control, and more specifically relates to a control apparatus that performs auxiliary control for the air/fuel ratio of the engine using a neural network.
2. Description of the Prior Art
As the number of automobiles has increased in recent years, so has the significance of the problems relating to is exhaust pollution. Many conventional cars have been fitted with catalytic converters to reduce the amount of poisonous gases, ouch as No.sub.x, CO, and HC, present in exhaust fumes, with three-way catalytic converters being a representative example of such. A problem, however, for such three-way converters is that the amounts of these gases produced in the engine's cylinders each depends on the air/fuel ratio (the ratio of the flow of air to fuel) in the cylinders of the engine. Here, when a lean (oxygen-rich) air/fuel ratio is used, the catalysts' conversion efficiency for No.sub.x falls off, while when a rich (oxygen-poor) air/fuel ratio is used, the catalysts' conversion efficiencies for HC and CO fall off. As a result, it is essential to keep the air/fuel ratio at a fixed value that enables the most effective conversion of the polluting gases by the three-way catalyst.
Here, the air/fuel ratio must be controlled to keep it at the fixed value regardless of the operational state of the engine. Such air/fuel ratio control performs feed-forward control when the driver adjusts the throttle to make compensated increases and decreases in the amount of injected fuel. It is also common for air/fuel ratio control to perform feedback control of a compensatory amount of injected fuel using readings given by an O.sub.2 sensor (which is an air/fuel ratio sensor) and a linear air/fuel ratio sensor (hereinafter referred to as an LAF sensor). These control operations are especially effective under normal driving conditions, such as when the engine is idling or being driven at a constant speed.
In reality, however, even if the fuel is injected into the air intake by an injector, not all of the fuel will flow into the cylinders, with there being complications such as some of the fuel sticking to the walls of the air intake pipe. The amount of gasoline coating the pipes exhibits a complex relation with the driving conditions (such as engine RPM (revolutions per minute) and load (air pressure of the air intake)) and the external environment (such as air intake temperature, cooling water temperature, and atmospheric pressure), with the amount of this coating fuel that evaporates and flows into the cylinders also depending on various factors such as the driving conditions and external environment. As a result, it has been very difficult to accurately control the air/fuel ratio when the engine condition is in a transitional state, which here refers to acceleration or deceleration, using only simple feed-forward and feedback control.
In order to improve the precision of such control, Japanese Laid-Open Patent Application 3-235723 discloses the use or a neural network to study the nonlinear aspects, such as the fuel coating, and to calculate a compensatory amount of injected fuel to increase the responsiveness of the control apparatus to changes in the operational state of the engine.
FIG. 1 shows a common example of the construction of a fuel-air ratio control apparatus that uses a neural network. This will be described in outline below. The air/fuel ratio is kept at a fixed level by feed-forward and feedback control which are performed by a control apparatus which has not been shown in the drawing. However, the construction shown in FIG. 1 is also provided and is used to keep the air/fuel ratio at an appropriate value when the engine is in a transitional state.
Conventional air/fuel ratio control apparatuses which use neural networks are equipped with a state detection unit 210 which detects various parameters which show the state of the engine, such as the engine RPM (Ne), the intake air pressure (Pe), the present throttle amount (THL), the injected fuel amount (Cf), the intake air temperature (Ta), the cooling water temperature (Tw), and the air/fuel ratio (A/F) itself. These detected parameters are inputted into a neural network which is designed to study the compensatory amount for the injected fuel amount as its output. This compensatory amount (.DELTA.Gf) for the injected fuel amount calculated by the neural network is estimated by the compensatory fuel amount estimating unit 220. This estimated compensatory fuel amount (.DELTA.Gf) is added to an injected fuel amount (Gf) which is calculated by the control apparatus (not illustrated) to amend the injected fuel amount, and in doing so supplement the control of the air/fuel ratio (A/F). By doing so, more precise control of the air/fuel ratio becomes possible for more complex transitional engine conditions.
However, conventional air/fuel control systems which use neural networks have the output of the air/fuel ratio (A/F) sensor as an input parameter of the neural network, although there are conditions, such as in extreme low temperatures or when the engine has just been started, where this sensor is inactive and so cannot be used. This results in the problem that supplementary control cannot be performed using the neural network until the engine warms up. Even after the engine has warmed up, there is still the problem that the use of the air/fuel ratio sensor for several years will result in a sharp fall in its performance, which will result in discrepancies in the output value of the neural network and reduce the performance of the whole air/fuel ratio control system. This can also lead to irregularities and deterioration of the various components in the air/fuel ratio control system.
There are also cases when too great or too little an amount of fuel is injected into the engine during starting, both of which result in the air/fuel mixture failing to ignite. Here, both cases result in the same result, but the output value of the air/fuel ratio sensor will show that the air/fuel ratio is too lean. Since conventional air/fuel ratio control system: are not able to take into account the various possible causes for the ignition failure of the air/fuel mixture, they will use the detected air/fuel ratio as it is, leading to inaccurate control and the risk that the system will be unable to prevent ignition failures which in turn leads to danger that potentially harmful unburnt fuel will be expelled from the exhaust.
Here, if the circumstances for ignition failures were known, it would be possible to adjust the injected amount of fuel to prevent ignition failures and so keep the operation of the engine smooth. In reality, the detection of the circumstances for an ignition failure during starting would require a pressure sensor to be fitted in each cylinder of the engine. This would raise the cost of the engine and so is not an economically viable option.
When driving their cars, car owners fill the tank with one out of a variety of commercially available brands of gasoline, each of which has different characteristics, such as its evaporation rate. Here, it is possible to conceive an air/fuel ratio control method which takes these differences in evaporation rates, and in particular the great differences in evaporation rates at low temperatures. However, car manufactures are unable to know what gasoline is used by car owners, and, to prevent ignition failures when the engine is cold, adjust the injected fuel amount during starting in accordance with the characteristics of the gasoline which has the worst evaporation rate. As a result, since most drivers will usually use a gasoline with a higher evaporation rate, the injected fuel amount will be higher than necessary, leading to the danger that potentially harmful unburnt fuel will be expelled from the exhaust.
Also, during operation the injected fuel amount in the engine is calculated by feed forward control that is fixed for a classification of gasoline based on the evaporation rate, so that if a different classification of gasoline is used, precise air/fuel ratio control cannot be performed. In particular, the air/fuel ratio sensor will not work at low temperatures, so that compensation using feedback Control and air/fuel ratio control using a conventional neural network will not be possible.